While deep learning has made huge advancements in most areas of speech synthesis and processing over the last decade, the progress of speech quality models due to deep learning has only recently been notable. One of the reasons for this is the limited amount of subjective ratings data that is publicly available. In the past few years several notable models have appeared, including DNSMOS [1]} and NISQA [2]}. Both DNSMOS and NISQA are from Microsoft, which has managed to assemble a very large dataset on the order of 100k utterances, and has recently released a public subset of the data that may encourage more neural models. Additionally, MOSNet [3]} is a deep learning-based MOS estimator for voice conversion. Semi-supervised architectures such as one using clustering [4]} and SESQA [5]} provide useful methods for dealing with the quality label data shortage, and lightweight and interpretable models that work with smaller amounts of data [6]}. The advancements in transformer-based pretrained models such as wav2vec 2.0 [7]} and HuBERT [8]} enable researchers to explore another semi-supervised method to take advantage of the large amount of speech data that exists without subjective labels. SSL-MOS [9]} was one of the baselines provided by the challenge organizers which uses wav2vec 2.0 with a minimal extra layer with promising results.
